Monitoring and predictive maintenance of an industrial line

Industry 4.0 push forward the development of concepts such as artificial intelligence, big data, and Industrial Internet of Things, which claims an evolution of the monitoring systems design in terms of the accessibility to the information. In this project, the author describes the design of a condition monitoring system to monitor the state of different components of an extrusion line and propose a system that allows predictive maintenance in industry, specifically an extrusion system. In this framework, Condition Based Maintenance, CBM, the health state of the component is continuously monitored. In some cases, the monitoring can be periodical. The goal is to make repairs in the opportune moment, by receiving data of malfunction, so the efficiency is maxed. The aim is to develop a system to monitor the state of different components of an extrusion line and to propose a system that allows predictive maintenance in industry and that the project can be used as a guideline to a complete condition monitoring system implementation in an industrial environment. To be able to achieve this, some first steps must be accomplished. These are a Taxonomy, or a breakdown of the system into individual elements and how they are related; and carrying out a Failure Modes Effect and Criticality Analysis, known as FMECA. With these studies, the author displays the failure modes that are critical for the operating of the system and thus, which have the most likelihood to occur while having a big impact. From the information extracted the author presents a model based in accelerometer, temperature and MCSA (Motor Current Spectral Analysis) sensors. Furthermore, the data obtained will need to be analysed. With that in mind, the operating frequencies as well as the failure modes frequencies must be studied, which will allow correct identification while analysing the data. This analysis will be done by characterizing data and applying analysing techniques as FFT or Hilbert